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Performance Framework for Virtual Machine Migration in Cloud Computing

Computers, Materials & Continua 2023年第3期74卷 6289-6305页

作者： Tahir Alyas Taher M.Ghazal badria sulaiman alfurhood Munir Ahmad Ossma Ali Thawabeh Khalid Alissa Qaiser Abbas Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797UAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebansaan Malaysia(UKM)Bangi43600SelangorMalaysia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Dean of Student Affairs Skyline University CollegeUniversity City SharjahSharjah1797UAE Networks and Communications Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Faculty of Computer and Information Systems Islamic University Madinah Madinah42351Saudi Arabia

In the cloud environment,the transfer of data from one cloud server to another cloud server is called *** can be delivered in various ways,from one data centre to *** research aims to increase the migration performance of the virtual machine(VM)in the cloud *** allow cloud customers to store essential data and ***,server usage has grown dramatically due to the virtualization of computer systems,resulting in higher data centre power consumption,storage needs,and operating *** VMs on one data centre manage share resources like central processing unit(CPU)cache,network bandwidth,memory,and application ***-cloud,VMmigration addresses the performance degradation due to cloud server configuration,unbalanced traffic load,resource load management,and fault situations during data *** speed is influenced by the size of the VM,the dirty rate of the running application,and the latency ofmigration *** a result,evaluating VM migration performance while considering all of these factors becomes a difficult *** effort of this research is to assess migration problems on *** simulation results in Matlab show that if the VMsize grows,themigration time of VMs and the downtime can be impacted by three orders *** dirty page rate decreases,themigration time and the downtime grow,and the latency time decreases as network bandwidth increases during the migration time and post-migration overhead calculation when the VMtransfer is *** the simulated cases of VMs migration were performed in a fuzzy inference system with performance graphs.

关键词： Latency cloud computing dirty page ratio storage migration performance

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Optimizing Resource Allocation Framework for Multi-Cloud Environment

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Computers, Materials & Continua 2023年第5期75卷 4119-4136页

作者： Tahir Alyas Taher M.Ghazal badria sulaiman alfurhood Ghassan F.Issa Osama Ali Thawabeh Qaiser Abbas Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebansaan Malaysia(UKM)43600BangiSelangorMalaysia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Skyline University College University City Sharjah1797SharjahUAE Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia

Cloud computingmakes dynamic resource provisioning more *** a functioning service is crucial,and changes are made when particular criteria are *** research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service(QoS),estimating the required resources,and modifying allotted resources depending on workload and parallelism due to *** allocation is a complex challenge due to the versatile service providers and resource *** engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of *** objective of a coherent and rational resource allocation is to attain the quality of *** also includes identifying critical parameters to develop a resource allocation mechanism.A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud *** three main parameters of the proposed framework are data accessibility,optimization,and *** an optimization technique,these three segments are further divided into subsets for resource allocation and long-term service *** CloudSim simulator has been used to validate the suggested *** experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained *** results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined.

关键词： Multi-cloud query optimization cloud resources allocation modelling virtualization

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Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

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Computers, Materials & Continua 2022年第4期71卷 143-157页

作者： Anwer Mustafa Hilal badria sulaiman alfurhood Fahd N.Al-Wesabi Manar Ahmed Hamza Mesfer Al Duhayyim Huda G.Iskandar Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia

Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living andsustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install ITplatforms to collect and examine massive quantities of data. At the same time,it is essential to design effective artificial intelligence (AI) based tools to handlehealthcare crisis situations in smart cities. To offer proficient services to peopleduring healthcare crisis time, the authorities need to look closer towardsthem. Sentiment analysis (SA) in social networking can provide valuableinformation regarding public opinion towards government actions. With thismotivation, this paper presents a new AI based SA tool for healthcare crisismanagement (AISA-HCM) in smart cities. The AISA-HCM technique aimsto determine the emotions of the people during the healthcare crisis time, suchas COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides,brain storm optimization (BSO) with deep belief network (DBN), called BSODBN model is employed for feature extraction. Moreover, beetle antennasearch with extreme learning machine (BAS-ELM) method was utilized forclassifying the sentiments as to various classes. The use of BSO and BASalgorithms helps to effectively modify the parameters involved in the DBNand ELM models respectively. The performance validation of the AISA-HCMtechnique takes place using Twitter data and the outcomes are examinedwith respect to various measures. The experimental outcomes highlighted theenhanced performance of the AISA-HCM technique over the recent state ofart SA approaches with the maximum precision of 0.89, recall of 0.88, Fmeasure of 0.89, and accuracy of 0.94.

关键词： Smart city sentiment analysis artificial intelligence healthcare management metaheuristics deep learning parameter tuning

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An Optimized Deep Learning Model for Emotion Classification in Tweets

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Computers, Materials & Continua 2022年第3期70卷 6365-6380页

作者： Chinu Singla Fahd NAl-Wesabi Yash Singh Pathania badria sulaiman alfurhood Anwer Mustafa Hilal Mohammed Rizwanullah Manar Ahmed Hamza Mohammad Mahzari Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaIndia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

The task of automatically analyzing sentiments from a tweet has more use now than ever due to the spectrum of emotions expressed from national leaders to the average *** this data can be critical for any *** are often expressed with different intensity and topics which can provide great insight into how something affects *** analysis in Twittermitigates the various issues of analyzing the tweets in terms of views expressed and several approaches have already been proposed for sentiment analysis in *** used for analyzing tweet emotions are also briefly presented in literature survey *** this paper,hybrid combination of different model’s LSTM-CNN have been proposed where LSTMis Long Short TermMemory andCNNrepresents ConvolutionalNeural ***,the main contribution of our work is to compare various deep learning and machine learning models and categorization based on the techniques *** main drawback of LSTM is that it’s a timeconsuming process whereas CNN do not express content information in an accurate way,thus our proposed hybrid technique improves the precision rate and helps in achieving better *** step of our mentioned technique is to preprocess the data in order to remove stop words and unnecessary data to improve the efficiency in terms of time and accuracy also it shows optimal results when it is compared with predefined approaches.

关键词： Meta level features lexical mistakes sentiment analysis count vector natural language processing deep learning machine learning naive bayes

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